Methods and systems of evaluating a risk of a gastrointestinal cancer

ABSTRACT

A method of evaluating gastrointestinal cancer risk. The method comprises generating a set of features comprising a plurality of current blood test results from a blood collected from a target individual, providing at least one classifier generated according to an analysis of a plurality of respective historical blood test results of each of another of a plurality of sampled individuals, and evaluating, using a processor, a gastrointestinal cancer risk of the target individual by classifying the set of features using the at least one classifier.

FIELD AND BACKGROUND OF THE INVENTION

The present invention, in some embodiments thereof, relates to cancerdiagnosis and, more particularly, but not exclusively, to methods andsystems of evaluating a risk of cancer.

A diagnosis of colorectal cancer includes diagnosis based on theimmunological fecal occult blood reaction, diagnosis by colonoscopy, andthe like. However, diagnosis based on a fecal occult blood test does notserve as definitive diagnosis, and most of the persons withpositive-finding are false-positive. Furthermore, in regard to earlycolorectal cancer, there is a concern that both the detectionsensitivity and the detection specificity become lower in the diagnosisbased on a fecal occult blood test or the diagnosis by colonoscopy. Inparticular, early cancer in the right side colon is frequentlyoverlooked when diagnosed by a fecal occult blood test. Diagnosticimaging by CT (computer tomography), MRI (magnetic resonance imaging),PET (positron emission computerized-tomography) or the like is notsuitable for the diagnosis of colorectal cancer.

On the other hand, colorectal biopsy by colonoscopy serves as definitivediagnosis, but is a highly invasive examination, and implementingcolonoscopic examination at the screening stage is not practical.Furthermore, invasive diagnosis such as colonoscopy gives a burden toindividuals such as accompanying pain, and there may also be a risk ofbleeding upon examination, or the like.

During the last years, some new methods have been developed fordiagnosis of colorectal cancer. For example, U.S. Patent Publication No.US 2010/0009401 describes a method of evaluating colorectal cancer,where amino acid concentration data on the concentration value of aminoacid in blood collected from a subject to be evaluated is measured, anda colorectal cancer state in the subject is evaluated based on theconcentration value of at least one of Arg, Cys, Om, Trp, Glu, ABA, Val,Phe, Leu, GIn, Ile and His contained in the measured amino acidconcentration data of the subject.

SUMMARY OF THE INVENTION

According to some embodiments of the present invention, there areprovided a computerized method of evaluating gastrointestinal cancerrisk. The method comprises generating a set of features comprising aplurality of current blood test results from a blood collected from atarget individual, providing at least one classifier generated accordingto an analysis of a plurality of respective historical blood testresults of each of another of a plurality of sampled individuals; andevaluating, using a processor, a gastrointestinal cancer risk of thetarget individual by classifying the set of features using the at leastone classifier. Each of the plurality of historical and current bloodtest results comprises results of at least one the following bloodtests: red blood cells (RBC), hemoglobin (HGB), and hematocrit (HCT) andat least one result of the following blood tests hemoglobin (MCH) andmean corpuscular hemoglobin concentration (MCHC). Optionally, the bloodtest results are extracted from a complete blood count (CBC) test.

Optionally, the set of features comprises an age of the targetindividual; wherein the at least one classifier is generated accordingto an analysis of the age of each of another of a plurality of sampledindividuals.

Optionally, each of the plurality of historical and current blood testresults comprises results of red cell distribution width (RDW).

Optionally, each of the plurality of historical and current blood testresults comprises results of Platelets hematocrit (PCT).

Optionally, each of the plurality of historical and current blood testresults comprises results of mean cell volume (MCV).

Optionally, each of the plurality of historical and current blood testresults comprises at least one of the following blood tests: white bloodcell count—WBC (CBC); mean platelet volume (MPV); mean cell; plateletcount (CBC); eosinophils count; neutrophils percentage; monocytespercentage; eosinophils percentage; basophils percentage; andneutrophils count; monocytes count.

Optionally, the at least one classifier comprises a member of a groupconsisting of: a weighted linear regression classifier, a K-Nearestneighbors (KNN) classifier, and a random forest classifier.

Optionally, the set of features comprises at least one demographiccharacteristic of the target individual and the at least one classifiergenerated according to an analysis of respective the at least onedemographic characteristic of each of the plurality of sampledindividuals.

Optionally, the method further comprises selecting the at least oneclassifier according to at least one demographic characteristic of theindividual from a plurality of classifiers each generated according to aplurality of respective historical blood test results of sampledindividuals having at least one different demographic characteristic.

Optionally, the plurality of blood test results comprises at least oneresult from the following plurality of blood tests: biochemical bloodtest results may include any of the following blood test resultsAlbumin, Calcium, Chloride, Cholesterol, Creatinine, high densitylipoprotein (HDL), low density lipoprotein (LDL), Potassium, Sodium,Triglycerides, Urea, and/or Uric Acid.

According to some embodiments of the present invention, there areprovided a gastrointestinal cancer evaluating system. The systemcomprises a processor, a memory unit which stores at least oneclassifier generated according to an analysis of a plurality ofhistorical blood test results of each of another of a plurality ofsampled individuals, and an input unit which receives a plurality ofcurrent blood test results taken from a blood of a target individual,and a gastrointestinal cancer evaluating module which evaluates, usingthe processor, a gastrointestinal cancer risk of the target individualby classifying, using the at least one classifier, a set of featuresextracted from the plurality of current blood test results. Theplurality of historical and current blood test results comprises resultsof at least one the following of plurality of blood tests: red bloodcells (RBC), hemoglobin (HGB), and hematocrit (HCT) and at least oneresult of the following blood tests hemoglobin (MCH) and meancorpuscular hemoglobin concentration (MCHC).

Optionally, each of the plurality of historical and current blood testresults comprises results of red cell distribution width (RDW).

Optionally, each of the plurality of historical and current blood testresults comprises results of Platelets hematocrit (PCT).

Optionally, each of the plurality of historical and current blood testresults comprises of mean cell volume (MCV).

According to some embodiments of the present invention, there areprovided a method of generating a classifier for a CRC risk evaluation.The method comprises providing a plurality of historical blood testresults of each of another of a plurality of sampled individuals,generating a dataset having a plurality of sets of features each setgenerated according to respective plurality of historical blood testresults of another the plurality of sampled individuals, generating atleast one classifier according to an analysis the dataset, andoutputting the at least one classifier.

Optionally, the generating comprises calculating and adding at least onemanipulated version of an historical blood test result taken from arespective the plurality of historical blood test results as a featureto respective the set of features.

Optionally, the generating comprises weighting each the set of featuresaccording to a date of the respective plurality of historical blood testresults.

Optionally, the generating comprises filtering the plurality of sets offeatures to remove outliers according to a standard deviation maximumthreshold.

Optionally, the plurality of sets of features are weighted according toa date of the respective plurality of historical blood test results.

Optionally, the plurality of blood test results of at least one thefollowing blood tests: red blood cells (RBC), hemoglobin (HGB), andhematocrit (HCT) and at least one result of the following blood testshemoglobin (MCH) and mean corpuscular hemoglobin concentration (MCHC).

Optionally, each of the plurality of historical and current blood testresults comprises results of red cell distribution width (RDW).

Optionally, each of the plurality of historical and current blood testresults comprises results of Platelets hematocrit (PCT).

Optionally, each of the plurality of historical and current blood testresults comprises results of mean cell volume (MCV).

More optionally, the method further comprises adding at least onedemographic parameter of each of the plurality of sampled individuals toa respective the set of features.

More optionally, the at least one demographic parameter is a member of agroup consisting of gender, age, residential zone, race andsocio-economic characteristic.

More optionally, the generating comprises calculating and adding atleast one manipulated version of the at least one demographic parameteras a feature to respective the set of features.

Unless otherwise defined, all technical and/or scientific terms usedherein have the same meaning as commonly understood by one of ordinaryskill in the art to which the invention pertains. Although methods andmaterials similar or equivalent to those described herein can be used inthe practice or testing of embodiments of the invention, exemplarymethods and/or materials are described below. In case of conflict, thepatent specification, including definitions, will control. In addition,the materials, methods, and examples are illustrative only and are notintended to be necessarily limiting.

Implementation of the method and/or system of embodiments of theinvention can involve performing or completing selected tasks manually,automatically, or a combination thereof. Moreover, according to actualinstrumentation and equipment of embodiments of the method and/or systemof the invention, several selected tasks could be implemented byhardware, by software or by firmware or by a combination thereof usingan operating system.

For example, hardware for performing selected tasks according toembodiments of the invention could be implemented as a chip or acircuit. As software, selected tasks according to embodiments of theinvention could be implemented as a plurality of software instructionsbeing executed by a computer using any suitable operating system. In anexemplary embodiment of the invention, one or more tasks according toexemplary embodiments of method and/or system as described herein areperformed by a data processor, such as a computing platform forexecuting a plurality of instructions. Optionally, the data processorincludes a volatile memory for storing instructions and/or data and/or anon-volatile storage, for example, a magnetic hard-disk and/or removablemedia, for storing instructions and/or data. Optionally, a networkconnection is provided as well. A display and/or a user input devicesuch as a keyboard or mouse are optionally provided as well.

BRIEF DESCRIPTION OF THE DRAWINGS

Some embodiments of the invention are herein described, by way ofexample only, with reference to the accompanying drawings. With specificreference now to the drawings in detail, it is stressed that theparticulars shown are by way of example and for purposes of illustrativediscussion of embodiments of the invention. In this regard, thedescription taken with the drawings makes apparent to those skilled inthe art how embodiments of the invention may be practiced.

In the drawings:

FIG. 1 is a flowchart of a method of generating one or more classifiersfor estimating a gastrointestinal cancer risk score according to ananalysis of a plurality of individual records, according to someembodiments of the present invention;

FIG. 2 is a schematic illustration of a system for generating one ormore classifiers, for example by implementing the method depicted inFIG. 1, according to some embodiments of the present invention;

FIG. 3 is a receiving operating characteristic (ROC) curve graph,according to some embodiments of the present invention;

FIGS. 4A-4C are tables summarizing the performances of the differentexemplary classifiers, according to some embodiments of the presentinvention;

FIG. 5A is an image of a table of an expended set of features which arelisted according to their importance in a random forest classifier formen;

FIG. 5B is a table indicating correlation between pairs of results ofblood tests;

FIG. 6 is an image of a table showing performances for severaltime-windows.

FIG. 7 is an image of a table showing performances of a Random Forestclassifier;

FIG. 8 is a flowchart of a method of using a classifier(s) forestimating a gastrointestinal risk score for a target individual,according to some embodiments of the present invention;

FIG. 9 is a table indicating the performances of the classifiers foreach one of colon, stomach, rectum, and esophagus cancers in differentsensitivities for different groups of populations, according to someembodiments of the present invention; and

FIG. 10 is a set of tables summarizing an analysis of the results ofusing the above described classifiers for classifying anemic and notanemic individuals (white Americans), according to some embodiments ofthe present invention.

DESCRIPTION OF EMBODIMENTS OF THE INVENTION

The present invention, in some embodiments thereof, relates to cancerdiagnosis and, more particularly, but not exclusively, to methods andsystems of evaluating a risk of cancer.

According to some embodiments of the present invention, there areprovided methods and systems of evaluating gastrointestinal cancer riskby classifying a set of current blood test results of a targetindividual using one or more classifiers which are generated accordingto an analysis of historical blood test results of a plurality ofindividuals. The set of current blood test results includes at least oneresult of the following blood tests hemoglobin (HGB), hematocrit (HCT),and red blood cells (RBC) and at least one result of the following bloodtests mean cell hemoglobin (MCH) and mean corpuscular hemoglobinconcentration (MCHC) and the age of the target individual. Optionally,the set of current blood test results further includes one or more ofthe following blood tests: white blood cell count—WBC (CBC); meanplatelet volume (MPV); mean cell volume (MCV); red cell distributionwidth (RDW); platelet count (CBC); eosinophils count; neutrophilspercentage; monocytes percentage; eosinophils percentage; basophilspercentage; neutrophils count; monocytes count; and Platelets hematocrit(PCT).

Optionally, the gastrointestinal cancer risk is evaluated by classifyingbiochemical blood test results of the target individual. In suchembodiments, the classifiers are generated according to an analysis ofhistorical biochemical blood test results of the plurality ofindividuals. The biochemical blood test results may include results ofany of the following blood tests: Albumin, Calcium, Chloride,Cholesterol, Creatinine, high density lipoprotein (HDL), low densitylipoprotein (LDL), Potassium, Sodium, Triglycerides, Urea, and/or UricAcid.

Optionally, the gastrointestinal cancer risk is evaluated by classifyingdemographic characteristics of the target individual. In suchembodiments, the classifiers are generated according to an analysis ofdemographic characteristics of the plurality of individuals.

Optionally, both the current blood test results of the target individualand the historical blood test results of sampled individuals are usedfor generating expended sets of features which include manipulatedand/or weighted values. Optionally, each expended set of features isbased on the demographic characteristics of a respective individual, forexample as described below.

Optionally, the one or more classifiers are adapted to one or moredemographic characteristics of the target individual. Optionally, theclassifiers are selected to match one or more demographiccharacteristics of the target individual. In such embodiments, differentclassifiers may be used for women and men.

According to some embodiments of the present invention, there areprovided methods and systems of generating one or more classifiers forgastrointestinal risk evaluation. The methods and systems are based onanalysis of a plurality of historical blood test results of each ofanother of a plurality of sampled individuals and generating accordinglya dataset having a plurality of sets of features each generatedaccording to respective historical blood test results. The dataset isthen used to generate and output one or more classifiers, such asK-Nearest neighbors (KNN) classifiers, random forest classifiers, andweighted linear regression classifiers. The classifiers may be providedas modules for execution on client terminals or used as an onlineservice for evaluating gastrointestinal cancer risk of targetindividuals based on their current blood test results.

Before explaining at least one embodiment of the invention in detail, itis to be understood that the invention is not necessarily limited in itsapplication to the details of construction and the arrangement of thecomponents and/or methods set forth in the following description and/orillustrated in the drawings and/or the Examples. The invention iscapable of other embodiments or of being practiced or carried out invarious ways.

Reference is now made to FIG. 1, which is a flowchart of a method 100 ofgenerating one or more classifiers for estimating a gastrointestinalcancer risk score according to an analysis of a plurality of historicaltest results of each of a plurality of diagnosed individuals, accordingto some embodiments of the present invention. As used herein, agastrointestinal cancer may be colon, stomach, rectum, or esophaguscancer. Reference is also made to FIG. 2, which is a schematicillustration of a system 200 for generating classifier(s) for estimatinggastrointestinal cancer risk scores, for example by implementing themethod depicted in FIG. 1, according to some embodiments of the presentinvention.

The system 200 includes to one or more medical record database(s) 201and/or connected to a medical record database interface. The database(s)201 include a plurality of individual records, also referred to as aplurality of individual samples, which describe, for each of another ofa plurality of sampled individuals, one or more sets of a plurality ofhistorical test results each set of another individual, and optionallyone or more demographic parameter(s) and a gastrointestinal cancerprognosis. The set of a plurality of historical test results,demographic parameter(s), such as age, and/or gastrointestinal cancerprognosis may be stored in a common sample record and/or gathered from anumber of independent and/or connected databases. Optionally, thegastrointestinal cancer prognosis is a binary indication set accordingto a cancer registry record. The different test results may be ofcommonly performed blood tests and/or blood tests held during the sameperiod. Optionally, some sets of a plurality of historical test resultshave missing blood test results. These results are optionally completedby weighted averaging of the available blood test results of otherindividuals. The method further includes a processor 204, a classifiergeneration module 205, and an interface unit 206, such as a networkinterface.

As used herein, a demographic parameter includes age, gender, race,weight, national origin, geographical region of residence and/or thelike.

First, as shown at 101, one or more dataset(s) of a plurality ofindividual samples are provided.

Optionally, as shown at 102, the plurality of individual samples arescreened and/or selected according to matching criteria. For example,the sample records are of individuals in the age of 40 or older whoeither appear in a cancer registry with colon cancer, and optionallywithout other types of cancer, or do not appear in the not appear thereat the cancer registry. Optionally, sample records of individuals thatappear in the cancer registry are taken only if the latest set of aplurality of historical test results they document was taken during acertain period before the registration of a respective individual in thecancer registry, for example during a period of at least 30 days beforea current date and at most 2 years. Optionally, sample records ofindividuals that do not appear in the cancer registry are taken only ifthey include a set of a plurality of historical test results thatcreates an equal time-distribution (blood tests timing) for the positiveand negative gastrointestinal cancer populations. The process ofequating the time-distribution of the positive and negative samples alsoleads to omit at least some negative (non-registered) samples and to achange in the gastrointestinal prevalence in the data set.

Now, as shown at 103, an evaluation dataset, such as a matrix, isgenerated according to the sample data extracted from the samplerecords, for example by the classifier generation module 205. Theevaluation dataset includes a plurality of sets of features, optionallyexpended. Each set of features is generated from each one of thescreened and/or selected sample records. The set of features areoptionally unprocessed features which includes actual blood test and/ordemographic characteristic values.

As described above, each sample record includes one or more sets of aplurality of historical test results of a individual, each includes acombination blood test results, for example a combination of more than10, 15, 20 and/or any intermediate number of blood test results. In oneexample, each extracted set of unprocessed features includes at leastthe following 18 blood test results: red blood cells (RBC); white bloodcell count—WBC (CBC); mean platelet volume (MPV); hemoglobin (HGB);hematocrit (HCT); mean cell volume (MCV); mean cell hemoglobin (MCH);mean corpuscular hemoglobin concentration (MCHC); red cell distributionwidth (RDW); platelet count (CBC); eosinophils count; neutrophilspercentage; monocytes percentage; eosinophils percentage; basophilspercentage; neutrophils count; monocytes count; and Platelets hematocrit(PCT). In another example, each extracted set of unprocessed featuresincludes at least result of the following blood tests HGB, HCT, and RBC,at least one result of the following blood tests MCH and MCHC andadditional data reflecting the age of the target individual. Optionally,this extracted set of unprocessed features further includes one or moreof the following blood tests RDW, Platelets, and MCV. Additionally, thisextracted set of unprocessed features may further includes one or moreof the following blood tests WBC, eosinophils count, neutrophilspercentage and/or count, basophils percentage and/or count, andmonocytes percentage and/or count.

Optionally, the set of unprocessed features is expended. The expendedset of features contains features as the above unprocessed blood testresults and/or one or more demographic parameter(s) and optionallymanipulated blood test results and/or combination of blood test results,for instance as described below. For example, each feature in the set ofexpended features is based on a blood test result, a demographiccharacteristic, a combination of blood test result(s) and/or demographiccharacteristic(s), and/or a manipulation of blood test result(s) and/ordemographic characteristic(s). For example, when the set of unprocessedfeatures includes 18 test results, an expended set of 114 features isgenerated based on the following:

-   -   1. 18 features, each includes another of the 18 blood test        results.    -   2. 18 features, each includes a difference (e.g. a ratio)        between one of the 18 blood test results and a first virtual        result. The first virtual result is optionally calculated by a        weighted averaging of respective available results from the        sample records. Optionally, each available test is weighted        according to a period elapsed since the conducting thereof and        the target date, optionally a date of a set of a plurality of        historical test results of a target individual, referred to        herein as a target date. Optionally the available tests are test        taken during a first period, for example 540 days prior to the        target date. For example, a weight may be calculated as an        absolute value derived from time elapsed since the recording        (e.g. when the test was taken) thereof. The weight may be        calculated as a square function or any other function that is        monotonous to the absolute value.    -   3. 18 features each include a difference (e.g. a ratio) between        one of the 18 blood test results and a second virtual result,        which is optionally calculated as the above described first        virtual result, based on available tests taken during a second        period, for example during 1080 days prior to the target date.    -   4. 1 feature—the number of sets of a plurality of historical        test results the user performed during a period of year before        the target date.    -   5. 1 feature—the number of sets of a plurality of historical        test results the user performed during a period between 180*6        and 180*10 days prior to the target date.

6. 1 feature—the age of the individual, for example the individual'sbirth year.

7. 57 features which are squared values of all the above features(detailed in points 1-6).

Optionally, one or more biochemical blood test results may be documentedper individual and optionally added as feature to the set of features.These features may be treated as the blood test results above. Thebiochemical blood test results may include any of the following bloodtest results Albumin, Calcium, Chloride, Cholesterol, Creatinine, highdensity lipoprotein (HDL), low density lipoprotein (LDL), Potassium,Sodium, Triglycerides, Urea, and/or Uric Acid.

Optionally, different evaluation datasets, for example matrixes, havingdifferent sets of expended features are generated to create differentclassifiers which classify target individuals having differentdemographic characteristic(s), for example gender.

Optionally, the evaluation dataset, for example the matrix, is filtered,to remove iteratively outliers. Optionally, an average deviation and/ora standard deviation of each feature is calculated and features havingexceptional values, for example more than a standard deviation maximumthreshold, for example 10, are truncated to the standard deviationmaximum threshold. For example, the process is iteratively repeated 10times (or less if no truncations are performed). An exemplary pseudocode that describes the cleaning process is as follows—

Repeat 10 times   For each feature -     Calculate average and standarddeviation (sdv).     For each sample -       If (Value{sample,feature} > mean{feature}       +10*sdv{feature})         Value{sample,feature} = mean{feature} +9         *sdv{feature})       If(Value{sample, feature} < mean{feature} −       10*sdv{feature})        Value{sample, feature} = mean{feature} −         9*sdv{feature}    End samples loop   End features loop   Exit if no change was madeEnd of Repeat

Now, as shown at 104, the evaluation dataset is used for generatingclassifier(s) each classifying a gastrointestinal cancer risk of atarget individual based on one or more demographic characteristicsthereof and a current set of a plurality of test results, for example bythe classifier generation module 205. Optionally, one or more of thefollowing classifiers may be generated based on the evaluation dataset:

a weighted linear regression classifier where positive sample recordsreceive a score that is about 100 times the score of negative samplerecords;

a K-Nearest neighbors (KNN) classifier, for example 100 timesdown-sampling of a negative sample record; and

a random forest classifier, for example where each tree is built using a2:1 ratio of negative to positive sample records.

Optionally, the performance of each one of the classifiers is estimatedusing a 10-fold cross validation process where the evaluation dataset,referred to herein as a population, is randomly split to ten equal-sizedparts. For each part, the following may be performed:

selecting acceptable sets of blood test results from 90% of populationnot in the respective part;

training a classifier according to the selected sets of blood testresults;

selecting sets of blood test results from a 10% of population in therespective part; and

using the classifier on the selected sets of blood test results from the10% of population.

Now, classifications, also referred to as predictions, are collected tomeasure performance of each classifier. For example, the measures ofperformance are selected according to a receiving operatingcharacteristic (ROC) curve, for example as depicted in FIG. 3.Optionally, specificity at different (5%, 10%, 20%, 50%, and 70%)sensitivity (recall) values are used for identifying the measures. Theperformances of the different exemplary classifiers are summarized inthe tables provided in FIGS. 4A-4C which respectively have an area undercurve (AUC) of 0.840±0.001, 0.820±0.001, and 0.833±0.001. It should benoted that FIG. 3 and FIGS. 4A-4C are calculated based on an overallpopulation of 217,246 men of over 40, 1,415 have been identified ashaving positive colorectal cancer (CRC). Optionally, each one of thenumbers in the table (Lift, Est. Precision, and Specificity) representsmean±a standard deviation (std) calculated over different crossvalidation schemes, for example 10.

As used herein, a recall denotes a true positive (TP) rate of TPclassified individuals that equals to a percentage of CRC individuals(both TP and false negative (FN)), for example TP/(TP+FN). As usedherein, precision denotes a percentage of population having classifiedas having CRC for example TP/(TP+FP). As used herein, a lift denotes aratio of precision to overall CRC prevalence in the population. Forexample, among an overall population of 217,246 men of over 40, 1,415have been identified as having positive CRC. In this example, theprevalence is 0.65%. The selection of negative samples to create thecorrect time-distribution (see above), creates a bias in the learningand testing populations, leading to CRC prevalence of 1.2%. Thus, thelift may be directly found, but only indirectly used to conclude theestimated precision by adjusting a measured precision.

Optionally, the set of features which are used by a certain classifierfor classification are weighted according to a classificationimportance. The importance of a feature may be determined as an averagevalue, over data trees, of a decrease of node impurities as measured bythe Gini coefficient (statistical dispersion) due to splits. As anexample, FIG. 5A depicts a table of an expended set of features whichare listed according to their importance in a random forest classifierfor men.

It should be noted that the historical blood test results of theclassifiers and the current blood test may include pairs of blood testresults of blood tests considered to be similar in nature. These pairsinclude one or more of the following pairs hemoglobin (HGB) andhematocrit (HCT), neutrophils percentage/count and lymphocytespercentage/count (i.e. extracted from a CBC test), MCV and MCH, RBC andhematocrit (HCT), RBC and MCV. Checking both blood results of such apair is not trivial as for the skilled in the art these blood resultshave common indications and no cumulative value and therefore theskilled in the art would have use only results of one member of the pairof similar blood test and not both members of the pair of similar bloodtest. The inventors surprisingly found that the correlation betweenmembers of such a pair is not absolute and that the contribution to theperformance of the above described classifier(s) is substantial. Forexample, see the table in FIG. 5B.

It should be noted that the performance of a classifier depends on timebetween the last set of blood test results date and the cancer discoverydate, for example as registered in the cancer registry. This dependencyis captured by considering classifiers where acceptable blood tests forpositive samples are within limited time-windows (for example 30-90 daysprior to cancer registry, 90-180 days, and/or the like). For example,FIG. 6 depicts a table showing performances for several time-windows.The table shows age adjusted CBC data taken 30-720 days before diagnosisfrom CRC cases compared with healthy control data indicating long-termvariations. In addition, for each case, the selected parametervalue/result was compared to the same parameter results 1.5 years (delta1.5) and 3 years (delta 3) prior to the selected parameter value/resultevaluation. As indicated by the table of FIG. 6, the specificity isreduced when the data is older.

It should be noted that anemia in the blood, may be caused by severalgastrointestinal conditions and other, with GI cancer being the leastcommon. Unexplained anemia is a major predictor for CRC in the elderlyand, together with hemorrhoids, is a common cause for delay in CRCdiagnosis. Blood loss is present in 60% of CRC cases and a daily loss ofas little as 3 ml in the stool can cause iron anemia. As nearly as 18%of CRC cases had anemia more than a year before diagnosis 14, however, asignificant proportion are not anemic 1. Positive occult blood test maybe present. However, fecal blood, the currently used for CRC screening,detects only current bleeding while in CRC, blood loss is chronic.

In Spell D W, Jones D V, Jr., Harper W F, David Bessman J. The value ofa complete blood count in predicting cancer of the colon. Cancer DetectPrey 2004; 28 (1):37-42 it is reported that 88% of CRC patients had atleast one blood abnormality. As such, attempts to predict CRC fromcomplete blood counts (CBC) are under active research. In aretrospective study on newly diagnosed CRC patients from which CBCparameters were available 0-122 days before diagnosis, it was shown thatred blood cell distribution width (RDW) was increased above the normalrange and had 84% sensitivity and 88% specificity, mainly for rightsided CRC cases. No improved sensitivity in combination with RDW,hemoglobin and mean corpuscular volume (MCV) was documented.

According to some embodiments of the present invention, the performanceof a classifier generated as described herein may be used forclassifying both individuals with anemia condition and individualswithout anemia condition. For example, FIG. 11 is a set of tablessummarizing an analysis of the results of using the above describedclassifiers for classifying anemic and not anemic individuals (whiteAmericans). The set includes a plurality of tables, each summarizing theprobability of anemic individual of a certain group of individuals inrelation to non anemic individual of another group of individuals. Thegroups are optionally divided based on a combination between the age ofthe blood results and the age of the individual. FIG. 10 shows evidencethat independent measures of blood counts parameters are related to CRCand that combined changes in CBC parameters, even subtle ones, withinthe normal range may be used as part of the CRC screening process, forexample by scoring, for individual with or without anemia.

Now, as shown at 105, the classifier(s) are outputted, optionally as amodule that allows classifying target individuals, for example by theinterface unit 206. Optionally, different classifiers are defined forindividuals having different demographic characteristics, for exampleone classifier for men and another for women. For example, while aclassifier that is based on a group of features from the above 18features is set for men, a Random Forest classifier without biochemistrytests is used for women. The Random Forest classifier has an AUC of0.833±0.001 and performances as depicted in FIG. 7 where precision isestimated according to total prevalence of 0.45%.

Reference is now made to FIG. 8, which is a flowchart 400 of a method ofusing classifier(s), such as the above classifier(s), for estimating agastrointestinal cancer risk score for a target individual, according tosome embodiments of the present invention. In use, the classifier(s) maybe hosted in a web server that receives the target individual data andevaluates, using a gastrointestinal cancer evaluating module that usesthe classifier(s), a gastrointestinal cancer risk score in a subject tobe evaluated. The target individual data may be received via acommunication network, such as the internet, from a client terminal,such as a laptop, a desktop, a Smartphone, a tablet and/or the like,which provides the set of blood test results and demographiccharacteristics of the subject or a reference to this target data.

First, as shown at 401 and 402, classifier(s) and a target individualdata are provided. The target individual data includes one or moredemographic parameter(s) and a set of a plurality of current blood testresults held in the target date, which includes a number of current testresults of a target individual. The target individual data may beinputted manually by a user, for example using a graphical userinterface (GUI), selected by a user, optionally using the GUI, and/orprovided automatically, for example by a computer aided diagnosis (CAD)module and/or system. Optionally, the target individual data includesthe number of sets of blood test results the user performed during thelast year, last decade and/or any intermediate period. Each one of thesets of blood test results includes blood test results, for example agroup selected from the above 18 different blood test results.

Now, as shown at 403, a set of target individual features is extractedfrom the target individual data and optionally extended as describedabove.

Then, as shown at 404, the classifier(s) is used to calculate agastrointestinal cancer risk score for the target individual byweighting each feature in the set of target individual features. Now, asshown at 405, the gastrointestinal cancer risk score is outputted.

It should be noted that above described classifiers may be used forestimating cancer risk score for gastrointestinal cancer may be colon,stomach, rectum, or esophagus cancer. For example, FIG. 9 is a tableindicating the performances of the classifiers for each one of colon,stomach, rectum, and esophagus cancers in different sensitivities fordifferent groups of populations. Groups of populations are definedaccording to a combination between an age of the respective test bloodresults (stated in days, for instance 90-540, 90-540, 30-270, and360-720 days. and the range of ages, for example 40-100 and 50-75. Itshould be noted that the table in FIG. 9 indicating the performances ofthe classifiers on a different population than used for the classifiersdocumented with reference to FIGS. 4A and 4B. In FIG. 9, the dataincludes results of blood tests from a total of 81,641 Englishindividuals over the age of 40 of which 3,099 were diagnosed with coloncancer, 1,286 with rectal cancer, 578 with gastric cancer and 1,061 withesophagus cancer.

It is expected that during the life of a patent maturing from thisapplication many relevant systems and methods will be developed and thescope of the term a processor, a display, and user interface is intendedto include all such new technologies a priori.

As used herein the term “about” refers to ±10%.

The terms “comprises”, “comprising”, “includes”, “including”, “having”and their conjugates mean “including but not limited to”. This termencompasses the terms “consisting of” and “consisting essentially of”.

The phrase “consisting essentially of” means that the composition ormethod may include additional ingredients and/or steps, but only if theadditional ingredients and/or steps do not materially alter the basicand novel characteristics of the claimed composition or method.

As used herein, the singular form “a”, “an” and “the” include pluralreferences unless the context clearly dictates otherwise. For example,the term “a compound” or “at least one compound” may include a pluralityof compounds, including mixtures thereof.

The word “exemplary” is used herein to mean “serving as an example,instance or illustration”. Any embodiment described as “exemplary” isnot necessarily to be construed as preferred or advantageous over otherembodiments and/or to exclude the incorporation of features from otherembodiments.

The word “optionally” is used herein to mean “is provided in someembodiments and not provided in other embodiments”. Any particularembodiment of the invention may include a plurality of “optional”features unless such features conflict.

Throughout this application, various embodiments of this invention maybe presented in a range format. It should be understood that thedescription in range format is merely for convenience and brevity andshould not be construed as an inflexible limitation on the scope of theinvention. Accordingly, the description of a range should be consideredto have specifically disclosed all the possible subranges as well asindividual numerical values within that range. For example, descriptionof a range such as from 1 to 6 should be considered to have specificallydisclosed subranges such as from 1 to 3, from 1 to 4, from 1 to 5, from2 to 4, from 2 to 6, from 3 to 6 etc., as well as individual numberswithin that range, for example, 1, 2, 3, 4, 5, and 6. This appliesregardless of the breadth of the range.

Whenever a numerical range is indicated herein, it is meant to includeany cited numeral (fractional or integral) within the indicated range.The phrases “ranging/ranges between” a first indicate number and asecond indicate number and “ranging/ranges from” a first indicate number“to” a second indicate number are used herein interchangeably and aremeant to include the first and second indicated numbers and all thefractional and integral numerals therebetween.

It is appreciated that certain features of the invention, which are, forclarity, described in the context of separate embodiments, may also beprovided in combination in a single embodiment. Conversely, variousfeatures of the invention, which are, for brevity, described in thecontext of a single embodiment, may also be provided separately or inany suitable subcombination or as suitable in any other describedembodiment of the invention. Certain features described in the contextof various embodiments are not to be considered essential features ofthose embodiments, unless the embodiment is inoperative without thoseelements.

Although the invention has been described in conjunction with specificembodiments thereof, it is evident that many alternatives, modificationsand variations will be apparent to those skilled in the art.Accordingly, it is intended to embrace all such alternatives,modifications and variations that fall within the spirit and broad scopeof the appended claims.

All publications, patents and patent applications mentioned in thisspecification are herein incorporated in their entirety by referenceinto the specification, to the same extent as if each individualpublication, patent or patent application was specifically andindividually indicated to be incorporated herein by reference. Inaddition, citation or identification of any reference in thisapplication shall not be construed as an admission that such referenceis available as prior art to the present invention. To the extent thatsection headings are used, they should not be construed as necessarilylimiting.

1. A computerized method of evaluating gastrointestinal cancer risk, comprising: generating a set of features comprising a plurality of current blood test results from a blood collected from a target individual; selecting at least one classifier according to at least one demographic characteristic of said target individual from a plurality of classifiers each generated according to a plurality of respective historical blood test results of a plurality of sampled individuals having at least one different demographic characteristic; said at least one classifier is generated according to an analysis of said plurality of respective historical blood test results of each of another of said plurality of sampled individuals; and evaluating, using a processor, a gastrointestinal cancer risk of said target individual by classifying said set of features using said at least one classifier; and wherein each of said plurality of historical and current blood test results comprises results of at least one the following blood tests: red blood cells (RBC), hemoglobin (HGB), and hematocrit (HCT) and at least one result of the following blood tests hemoglobin (MCH) and mean corpuscular hemoglobin concentration (MCHC).
 2. The method of claim 1, wherein said gastrointestinal cancer risk is for a cancer selected from a group consisting of colon cancer, stomach cancer, rectum cancer, and esophagus cancer.
 3. The method of claim 1, wherein said set of features comprises an age of said target individual; wherein said at least one classifier is generated according to an analysis of the age of each of another of a plurality of sampled individuals.
 4. The method of claim 1, wherein each of said plurality of historical and current blood test results comprises results of red cell distribution width (RDW).
 5. The method of claim 1, wherein each of said plurality of historical and current blood test results comprises results of Platelets hematocrit (PCT).
 6. The method of claim 1, wherein each of said plurality of historical and current blood test results comprises results of mean cell volume (MCV).
 7. The method of claim 1, wherein each of said plurality of historical and current blood test results comprises results of both HGB and HCT.
 8. The method of claim 1, wherein each of said plurality of historical and current blood test results comprises results of both neutrophils percentage/count and lymphocytes percentage/count.
 9. The method of claim 1, wherein each of said plurality of historical and current blood test results comprises results of both MCV and MCH.
 10. The method of claim 1, wherein each of said plurality of historical and current blood test results comprises results of both RBC and HCT.
 11. The method of claim 1, wherein each of said plurality of historical and current blood test results comprises results of both RBC and MCV.
 12. The method of claim 1, wherein each of said plurality of historical and current blood test results comprises at least one of the following blood tests: white blood cell count—WBC (CBC); mean platelet volume (MPV); mean cell; platelet count (CBC); eosinophils count; neutrophils percentage; monocytes percentage; eosinophils percentage; basophils percentage; and neutrophils count; monocytes count.
 13. The method of claim 1, wherein said at least one classifier comprises a member of a group consisting of: a weighted linear regression classifier, a K-Nearest neighbors (KNN) classifier, and a random forest classifier.
 14. The method of claim 1, wherein said set of features comprises at least one demographic characteristic of said target individual and said at least one classifier generated according to an analysis of respective said at least one demographic characteristic of each of said plurality of sampled individuals.
 15. (canceled)
 16. The method of claim 1, wherein said plurality of blood test results comprises at least one result from the following plurality of blood tests: biochemical blood test results may include any of the following blood test results Albumin, Calcium, Chloride, Cholesterol, Creatinine, high density lipoprotein (HDL), low density lipoprotein (LDL), Potassium, Sodium, Triglycerides, Urea, and/or Uric Acid.
 17. A computer readable medium comprising computer executable instructions adapted to perform the method of claim
 1. 18-22. (canceled)
 23. A method of generating a classifier for a gastrointestinal cancer risk evaluation, comprising: providing a plurality of historical blood test results of each of another of a plurality of sampled individuals; generating a dataset having a plurality of sets of features each set generated according to respective plurality of historical blood test results of another said plurality of sampled individuals; generating at least one classifier according to an analysis said dataset; and outputting said at least one classifier; wherein said generating comprises calculating and adding at least one manipulated version of an historical blood test result taken from a respective said plurality of historical blood test results as a feature to respective said set of features.
 24. A method of generating a classifier for a gastrointestinal cancer risk evaluation, comprising: providing a plurality of historical blood test results of each of another of a plurality of sampled individuals; generating a dataset having a plurality of sets of features each set generated according to respective plurality of historical blood test results of another said plurality of sampled individuals; generating at least one classifier according to an analysis said dataset; and outputting said at least one classifier; wherein said generating comprises weighting each said set of features according to a date of said respective plurality of historical blood test results.
 25. A method of generating a classifier for a gastrointestinal cancer risk evaluation, comprising: providing a plurality of historical blood test results of each of another of a plurality of sampled individuals; generating a dataset having a plurality of sets of features each set generated according to respective plurality of historical blood test results of another said plurality of sampled individuals; generating at least one classifier according to an analysis said dataset; and outputting said at least one classifier; wherein said generating comprises filtering said plurality of sets of features to remove outliers according to a standard deviation maximum threshold.
 26. A method of generating a classifier for a gastrointestinal cancer risk evaluation, comprising: providing a plurality of historical blood test results of each of another of a plurality of sampled individuals; generating a dataset having a plurality of sets of features each set generated according to respective plurality of historical blood test results of another said plurality of sampled individuals; generating at least one classifier according to an analysis said dataset; and outputting said at least one classifier; wherein said plurality of sets of features are weighted according to a date of said respective plurality of historical blood test results.
 27. The method of claim 24, wherein said plurality of blood test results of at least one the following blood tests: red blood cells (RBC), hemoglobin (HGB), and hematocrit (HCT) and at least one result of the following blood tests hemoglobin (MCH) and mean corpuscular hemoglobin concentration (MCHC).
 28. The method of claim 1, wherein each of said plurality of historical and current blood test results comprises results of red cell distribution width (RDW).
 29. The method of claim 1, wherein each of said plurality of historical and current blood test results comprises results of Platelets hematocrit (PCT).
 30. The method of claim 1, wherein each of said plurality of historical and current blood test results comprises results of mean cell volume (MCV).
 31. The method of claim 24, further comprising adding at least one demographic parameter of each of said plurality of sampled individuals to a respective said set of features.
 32. The method of claim 31, wherein said at least one demographic parameter is a member of a group consisting of gender, age, residential zone, race and socio-economic characteristic.
 33. A method of generating a classifier for a gastrointestinal cancer risk evaluation, comprising: providing a plurality of historical blood test results of each of another of a plurality of sampled individuals; generating a dataset having a plurality of sets of features each set generated according to respective plurality of historical blood test results of another said plurality of sampled individuals; adding at least one demographic parameter of each of said plurality of sampled individuals to a respective said set of features; generating at least one classifier according to an analysis said dataset; outputting said at least one classifier wherein said generating comprises calculating and adding at least one manipulated version of said at least one demographic parameter as a feature to respective said set of features. 